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Fault Self-Healing Cooperative Strategy of New Energy Distribution Network Based on Improved Ant Colony-Genetic Hybrid Algorithm 认领 引用 被引量：1
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作者 Fengchao Chen Aoqi Mei +2 位作者 Zheng Liu Ruhao Wu Qiwei Li 《Energy Engineering》 EI 2026年第4期247-267,共21页
With the high proportion of new energy access,the traditional fault self-healing mechanism of the distribution network is challenged.Aiming at the demand for fast recovery of new distribution network faults,this paper... With the high proportion of new energy access,the traditional fault self-healing mechanism of the distribution network is challenged.Aiming at the demand for fast recovery of new distribution network faults,this paper proposes a fault self-healing cooperative strategy for the new energy distribution network based on an improved ant colony-genetic hybrid algorithm.Firstly,the graph theory adjacency matrix is used to characterize the topology of the distribution network,and the dynamic positioning of new energy nodes is realized.Secondly,based on the output model and load characteristic model of wind,photovoltaic,and energy storage,a two-layer cooperative self-healing model of the distribution network is constructed.The upper layer is based on the improved depth-breadth hybrid search(DFS-BFS)to divide the island,with the maximum weight load recovery and the minimum number of switching actions as the goal,combined with the load priority to dynamically restore the key load.The lower layer uses the improved ant colony-genetic hybrid algorithm to solve the fault recovery path with the minimum total power loss load and the minimum network loss as the goal,generate the optimal switching sequence,and verify the power flow constraints.Finally,the simulation results based on the IEEE 33-bus system show that the proposed method can guarantee the power supply of key loads in the distribution network with high-tech energy penetration,restore the power supply of more load nodes with the least switching operation,and effectively reduce the line loss,which verifies the effectiveness and superiority of the method. 展开更多
关键词 Fault recovery identification of topology improved ant colony-genetic hybrid algorithm distribution network self-healing
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Energy-absorption forecast of thin-walled structure by GA-BP hybrid algorithm 认领 引用 被引量：11
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作者 谢素超 周辉 +1 位作者 赵俊杰 章易程 《Journal of Central South University》 SCIE EI CAS 2013年第4期1122-1128,共7页
In order to analyze the influence rule of experimental parameters on the energy-absorption characteristics and effectively forecast energy-absorption characteristic of thin-walled structure, the forecast model of GA-B... In order to analyze the influence rule of experimental parameters on the energy-absorption characteristics and effectively forecast energy-absorption characteristic of thin-walled structure, the forecast model of GA-BP hybrid algorithm was presented by uniting respective applicability of back-propagation artificial neural network （BP-ANN） and genetic algorithm （GA）. The detailed process was as follows. Firstly, the GA trained the best weights and thresholds as the initial values of BP-ANN to initialize the neural network. Then, the BP-ANN after initialization was trained until the errors converged to the required precision. Finally, the network model, which met the requirements after being examined by the test samples, was applied to energy-absorption forecast of thin-walled cylindrical structure impacting. After example analysis, the GA-BP network model was trained until getting the desired network error only by 46 steps, while the single BP-ANN model achieved the same network error by 992 steps, which obviously shows that the GA-BP hybrid algorithm has faster convergence rate. The average relative forecast error （ARE） of the SEA predictive results obtained by GA-BP hybrid algorithm is 1.543%, while the ARE of the SEA predictive results obtained by BP-ANN is 2.950%, which clearly indicates that the forecast precision of the GA-BP hybrid algorithm is higher than that of the BP-ANN. 展开更多
关键词 thin-walled structure GA-BP hybrid algorithm impact energy-absorption characteristic forecast
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A new hybrid algorithm for global optimization and slope stability evaluation 认领 引用 被引量：5
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作者 Taha Mohd Raihan Khajehzadeh Mohammad Eslami Mahdiyeh 《Journal of Central South University》 SCIE EI CAS 2013年第11期3265-3273,共9页
A new hybrid optimization algorithm was presented by integrating the gravitational search algorithm （GSA） with the sequential quadratic programming （SQP）, namely GSA-SQP, for solving global optimization problems a... A new hybrid optimization algorithm was presented by integrating the gravitational search algorithm （GSA） with the sequential quadratic programming （SQP）, namely GSA-SQP, for solving global optimization problems and minimization of factor of safety in slope stability analysis. The new algorithm combines the global exploration ability of the GSA to converge rapidly to a near optimum solution. In addition, it uses the accurate local exploitation ability of the SQP to accelerate the search process and find an accurate solution. A set of five well-known benchmark optimization problems was used to validate the performance of the GSA-SQP as a global optimization algorithm and facilitate comparison with the classical GSA. In addition, the effectiveness of the proposed method for slope stability analysis was investigated using three ease studies of slope stability problems from the literature. The factor of safety of earth slopes was evaluated using the Morgenstern-Price method. The numerical experiments demonstrate that the hybrid algorithm converges faster to a significantly more accurate final solution for a variety of benchmark test functions and slope stability problems. 展开更多
关键词 gravitational search algorithm sequential quadratic programming hybrid algorithm global optimization slope stability
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A Gridless-Finite Volume Hybrid Algorithm for Euler Equations 认领 引用 被引量：5
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作者 马志华 陈红全 吴晓军 《Chinese Journal of Aeronautics》 EI CAS 2006年第4期286-294,共9页
A fast hybrid algorithm based on gridless method coupled with finite volume method （FVM） is developed for the solution to Euler equations. Compared with pure gridless method, the efficiency of the hybrid algorithm i... A fast hybrid algorithm based on gridless method coupled with finite volume method （FVM） is developed for the solution to Euler equations. Compared with pure gridless method, the efficiency of the hybrid algorithm is improved to the level of finite volume method for most parts of the flow filed arc covered with grid cells. Moreover, the hybrid method is flexible to deal with the configurations as clouds of points are used to cover the region adjacent to the bodies. Mirror satellites and mirror grid cells arc introduced to the interface to accomplish data communication between the different parts of the flow field. The Euler Equations arc spatially discretized with finite volume method and gridless method in mesh and clouds of points respectively, and an explicit four-stage Runge-Kutta scheme is utilized to reach the steady-state solution. Internal flows in channels and external flows over airfoils arc investigated with hybrid method, and the solutions arc comparad to those using pure finite volume method and pure gridless method. Numerical examples show that the hybrid algorithm captures the shock waves accurately, and it is as efficient as fmite volume method. 展开更多
关键词 fluid mechanicsl hybrid algorithm： gridless method finite volume method Euler equations
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Short-term power generation scheduling rules for cascade hydropower stations based on hybrid algorithm 认领 引用 被引量：3
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作者 Wei XIE Chang-ming JI +1 位作者 Zi-jun YANG Xiao-xing ZHANG 《Water Science and Engineering》 EI CAS 2012年第1期46-58,共13页
Power generation dispatching is a large complex system problem with multi-dimensional and nonlinear characteristics. A mathematical model was established based on the principle of reservoir operation. A large quantity... Power generation dispatching is a large complex system problem with multi-dimensional and nonlinear characteristics. A mathematical model was established based on the principle of reservoir operation. A large quantity of optimal scheduling processes were obtained by calculating the daily runoff process within three typical years, and a large number of simulated daily runoff processes were obtained using the progressive optimality algorithm （POA） in combination with the genetic algorithm （GA）. After analyzing the optimal scheduling processes, the corresponding scheduling rules were determined, and the practical formulas were obtained. These rules can make full use of the rolling runoff forecast and carry out the rolling scheduling. Compared with the optimized results, the maximum relative difference of the annual power generation obtained by the scheduling rules is no more than 1%. The effectiveness and practical applicability of the scheduling rules are demonstrated by a case study. This study provides a new perspective for formulating the rules of power generation dispatching. 展开更多
关键词 scheduling rule short-time power generation dispatching, hybrid algorithm cascade hydropower station
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Multi-objective coordination optimal model for new power intelligence center based on hybrid algorithm 认领 引用 被引量：1
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作者 刘吉成 牛东晓 乞建勋 《Journal of Central South University》 SCIE EI CAS 2009年第4期683-689,共7页
In order to resolve the coordination and optimization of the power network planning effectively, on the basis of introducing the concept of power intelligence center (PIC), the key factor power flow, line investment a... In order to resolve the coordination and optimization of the power network planning effectively, on the basis of introducing the concept of power intelligence center (PIC), the key factor power flow, line investment and load that impact generation sector, transmission sector and dispatching center in PIC were analyzed and a multi-objective coordination optimal model for new power intelligence center (NPIC) was established. To ensure the reliability and coordination of power grid and reduce investment cost, two aspects were optimized. The evolutionary algorithm was introduced to solve optimal power flow problem and the fitness function was improved to ensure the minimum cost of power generation. The gray particle swarm optimization (GPSO) algorithm was used to forecast load accurately, which can ensure the network with high reliability. On this basis, the multi-objective coordination optimal model which was more practical and in line with the need of the electricity market was proposed, then the coordination model was effectively solved through the improved particle swarm optimization algorithm, and the corresponding algorithm was obtained. The optimization of IEEE30 node system shows that the evolutionary algorithm can effectively solve the problem of optimal power flow. The average load forecasting of GPSO is 26.97 MW, which has an error of 0.34 MW compared with the actual load. The algorithm has higher forecasting accuracy. The multi-objective coordination optimal model for NPIC can effectively process the coordination and optimization problem of power network. 展开更多
关键词 power intelligence center （PIC） coordination optimal model power network planning hybrid algorithm
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A New Hybrid Algorithm and Its Numerical Realization for a Quasi-nonexpansive Mapping 认领 引用 被引量：7
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作者 GAO XING-HUI MA LE-RONG Ji You-qing 《Communications in Mathematical Research》 CSCD 2017年第4期340-346,共7页
The purpose of this article is to propose a new hybrid projection method for a quasi-nonexpansive mapping. The strong convergence of the algorithm is proved in real Hilbert spaces. A numerical experiment is also inclu... The purpose of this article is to propose a new hybrid projection method for a quasi-nonexpansive mapping. The strong convergence of the algorithm is proved in real Hilbert spaces. A numerical experiment is also included to explain the effectiveness of the proposed methods. The results of this paper are interesting extensions of those known results. 展开更多
关键词 quasi-nonexpansive mapping hybrid algorithm strong convergence Hilbert space
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Enhanced Detection of Cerebral Atherosclerosis Using Hybrid Algorithm of Image Segmentation 认领 引用
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作者 Shakunthala Masi Helenprabha Kuttiappan 《Intelligent Automation & Soft Computing》 SCIE 2023年第4期733-744,共12页
In medical science for envisaging human body’s phenomenal structure a major part has been driven by image processing techniques.Major objective of this work is to detect of cerebral atherosclerosis for image segmenta... In medical science for envisaging human body’s phenomenal structure a major part has been driven by image processing techniques.Major objective of this work is to detect of cerebral atherosclerosis for image segmentation applica-tion.Detection of some abnormal structures in human body has become a difficult task to complete with some simple images.For expounding and distinguishing neural architecture of human brain in an effective manner,MRI(Magnetic Reso-nance Imaging)is one of the most suitable and significant technique.Here we work on detection of Cerebral Atherosclerosis from MRI images of patients.Cer-ebral Atherosclerosis is a cerebral vascular disease causes narrowing of the arteries due to buildup of fatty plaque inside the blood vessels of the brain.It leads to Ischemic stroke if not diagnosed early.Stroke affects majorly old age people and percentage of affected women is more compared to men.Results:Preproces-sing is done by using alpha trimmed meanfilter which is used to remove noise and also it enhances the image.Segmentation of cerebral atherosclerosis is done by using K-means clustering,Contextual clustering,and proposed Hybrid algo-rithm.Various parameters like Correlation,Pixel density,energy is determined and from the analysis of parameters it is determined that proposed Hybrid algo-rithm is efficient. 展开更多
关键词 Atherosclerosis Ischemic stroke Alpha trimmed meanfilter K-means Contextual clustering Hybrid algorithm
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Detecting and Preventing of Attacks in Cloud Computing Using Hybrid Algorithm 认领 引用
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作者 R.S.Aashmi T.Jaya 《Intelligent Automation & Soft Computing》 SCIE 2023年第1期79-95,共17页
Cloud computing is the technology that is currently used to provide users with infrastructure,platform,and software services effectively.Under this system,Platform as a Service(PaaS)offers a medium headed for a web de... Cloud computing is the technology that is currently used to provide users with infrastructure,platform,and software services effectively.Under this system,Platform as a Service(PaaS)offers a medium headed for a web development platform that uniformly distributes the requests and resources.Hackers using Denial of service(DoS)and Distributed Denial of Service(DDoS)attacks abruptly interrupt these requests.Even though several existing methods like signature-based,statistical anomaly-based,and stateful protocol analysis are available,they are not sufficient enough to get rid of Denial of service(DoS)and Distributed Denial of Service(DDoS)attacks and hence there is a great need for a definite algorithm.Concerning this issue,we propose an improved hybrid algorithm which is a combination of Multivariate correlation analysis,Spearman coefficient,and mitigation technique.It can easily differentiate common traffic and attack traffic.Not only that,it greatly helps the network to distribute the resources only for authenticated requests.The effects of comparing with the normalized information have shown an extra encouraging detection accuracy of 99%for the numerous DoS attack as well as DDoS attacks. 展开更多
关键词 Hybrid algorithm(HA) distributed denial of service(DDoS) denial of service(DoS) platform as a service(PaaS) infrastructure as a service(IaaS) software as a service(SaaS)
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Estimation of Vehicle Mass and Road Grade of Electric Vehicle Based on Hybrid Algorithm 认领 引用
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作者 ZHU Zong-kai HE Chao +1 位作者 LI Jia-qiang LIU Xue-yuan 《Journal of Highway and Transportation Research and Development(English Edition)》 2023年第2期48-55,共8页
In view of the parameter estimation of gross vehicle mass and road gradient during the driving of battery electric vehicles,according to the characteristics of the vehicle mass with steady variability and road gradien... In view of the parameter estimation of gross vehicle mass and road gradient during the driving of battery electric vehicles,according to the characteristics of the vehicle mass with steady variability and road gradient with time variability,on the basis of vehicle longitudinal dynamics,an method for estimating gross vehicle mass and road gradient based on a hybrid algorithm is proposed,and the method is applied to the starting stage of battery electric vehicles.The gross vehicle mass is estimated by using the forgetting factor recursive least square algorithm with good estimation efficacy for steady variables,and the output result of mass is regarded as one of the input parameters for gradient estimation.The road gradient is estimated with adaptive Kalman filtering,and the noise influence that cannot be concluded with external noise statistical characteristics is reduced by introducing a noise estimator with forgetting factor to improve the estimation accuracy of road gradient.The electric vehicle starting test is carried out on the selected flat ground and micro-slope sections,and air resistance is ignored based on the characteristics of low speed during vehicle starting.The required data are collected with the Global Navigation Satellite System(GNSS)terminal and vehicle controller area network(CAN)bus to perform offline calculations.The result shows that(1)a road section with variable gradient is selected to verify the validity of the algorithm,and the gross vehicle mass estimation result of the section is converged within 10 kg of the true value eventually with small error in the gradient estimation result;(2)the estimations of gross vehicle mass and changing of road gradient can be accurately estimated by using hybrid algorithm;(3)the mass estimation result of the twice starting test has the same convergence trend and the mass error is less than 2%,although the gradient estimation error is increased slightly,it is still less than 0.6%,which indicates the high estimation accuracy at starting stage of battery electric vehicle by using the hybrid algorithm. 展开更多
关键词 automobile engineering road gradient hybrid algorithm battery electric vehicle adaptive Kalman filtering
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A Hybrid Algorithm for TSP 认领 引用
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作者 Huang Zhangcan & Lu liuang 《Wuhan University Journal of Natural Sciences》 CAS 1996年第Z1期461-464,共4页
Abstract In this paper, we introduce a hybrid algorithm to search the approximate solution of TSP, which contains the advantage of Lin's algorithm, simulate annealing algorithm, genetic algorithm and Domain Decomp... Abstract In this paper, we introduce a hybrid algorithm to search the approximate solution of TSP, which contains the advantage of Lin's algorithm, simulate annealing algorithm, genetic algorithm and Domain Decomposition method. The algorithm has been successfully used in solving many TSP problems from 100 to 100000 cities. Those applications demonstrate the efficiency of the algorithm. 展开更多
关键词 A Hybrid Algorithm for TSP
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Multi-Objective Hybrid Sailfish Optimization Algorithm for Planetary Gearbox and Mechanical Engineering Design Optimization Problems 认领 引用 被引量：1
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作者 Miloš Sedak Maja Rosic Božidar Rosic 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第2期2111-2145,共35页
This paper introduces a hybrid multi-objective optimization algorithm,designated HMODESFO,which amalgamates the exploratory prowess of Differential Evolution(DE)with the rapid convergence attributes of the Sailfish Op... This paper introduces a hybrid multi-objective optimization algorithm,designated HMODESFO,which amalgamates the exploratory prowess of Differential Evolution(DE)with the rapid convergence attributes of the Sailfish Optimization(SFO)algorithm.The primary objective is to address multi-objective optimization challenges within mechanical engineering,with a specific emphasis on planetary gearbox optimization.The algorithm is equipped with the ability to dynamically select the optimal mutation operator,contingent upon an adaptive normalized population spacing parameter.The efficacy of HMODESFO has been substantiated through rigorous validation against estab-lished industry benchmarks,including a suite of Zitzler-Deb-Thiele(ZDT)and Zeb-Thiele-Laumanns-Zitzler(DTLZ)problems,where it exhibited superior performance.The outcomes underscore the algorithm’s markedly enhanced optimization capabilities relative to existing methods,particularly in tackling highly intricate multi-objective planetary gearbox optimization problems.Additionally,the performance of HMODESFO is evaluated against selected well-known mechanical engineering test problems,further accentuating its adeptness in resolving complex optimization challenges within this domain. 展开更多
关键词 Multi-objective optimization planetary gearbox gear efficiency sailfish optimization differential evolution hybrid algorithms
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Optimal scheduling of agricultural machines in hilly mountainous areas based on NSGA-Ⅱ-SA hybrid algorithm with applications 认领 引用 被引量：1
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作者 Huanyu Liu Jiahao Luo +2 位作者 Lihan Zhang Fulin Wang Shuang Wang 《International Journal of Agricultural and Biological Engineering》 SCIE 2025年第5期234-245,共12页
Optimizing the scheduling of farm machinery is essential to meet farmers’ requirements, minimize scheduling costs, and save time. This study focuses on scheduling farm machinery in multiple cooperatives across variou... Optimizing the scheduling of farm machinery is essential to meet farmers’ requirements, minimize scheduling costs, and save time. This study focuses on scheduling farm machinery in multiple cooperatives across various regions, aiming to minimize scheduling costs and reduce scheduling time. Initially, a multi-constraint hybrid clustering algorithm is employed to assign farmland to each farm machinery cooperative by clustering before scheduling. Subsequently, an enhanced version of the Non-dominated Sorting Genetic Algorithm-Ⅱ (NSGA-Ⅱ) is proposed, integrating a local search strategy based on congestion-based neighborhood search and the Simulated Annealing (SA) algorithm to develop the NSGA-Ⅱ-SA algorithm. This hybrid multi-objective evolutionary algorithm effectively optimizes scheduling costs and time. The model’s validity and the algorithm’s superiority are demonstrated through a Web-based multi-region agricultural machine scheduling system and an example study. Experimental results show that the NSGA-Ⅱ-SA algorithm significantly reduces scheduling costs and time, as well as the number of scheduled farm machines, outperforming other algorithms with reductions of 9.8%, 3.1%, and 8.7% in total scheduling costs, and 12.5%, 13.4%, and 11.6% in total scheduling time. This research establishes a theoretical framework for multi-region agricultural machine scheduling in hilly and mountainous areas, enhancing agricultural production efficiency. 展开更多
关键词 multi-region agricultural machine scheduling hilly mountainous area hybrid optimization algorithm agricultural machine scheduling system
Hybrid genetic algorithm for parametric optimization of surface pipeline networks in underground natural gas storage harmonized injection and production conditions 认领 引用
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作者 Jun Zhou Zichen Li +4 位作者 Shitao Liu Chengyu Li Yunxiang Zhao Zonghang Zhou Guangchuan Liang 《Natural Gas Industry B》 2025年第2期234-250,共17页
The surface injection and production system(SIPS)is a critical component for effective injection and production processes in underground natural gas storage.As a vital channel,the rational design of the surface inject... The surface injection and production system(SIPS)is a critical component for effective injection and production processes in underground natural gas storage.As a vital channel,the rational design of the surface injection and production(SIP)pipeline significantly impacts efficiency.This paper focuses on the SIP pipeline and aims to minimize the investment costs of surface projects.An optimization model under harmonized injection and production conditions was constructed to transform the optimization problem of the SIP pipeline design parameters into a detailed analysis of the injection condition model and the production condition model.This paper proposes a hybrid genetic algorithm generalized reduced gradient(HGA-GRG)method,and compares it with the traditional genetic algorithm(GA)in a practical case study.The HGA-GRG demonstrated significant advantages in optimization outcomes,reducing the initial cost by 345.371×104 CNY compared to the GA,validating the effectiveness of the model.By adjusting algorithm parameters,the optimal iterative results of the HGA-GRG were obtained,providing new research insights for the optimal design of a SIPS. 展开更多
关键词 Underground natural gas storage Surface injection and production pipeline Parameter optimization Hybrid genetic algorithm
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Hybrid genetic simulated annealing algorithm for agile Earth observation satellite scheduling considering cloud cover distribution 认领 引用
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作者 SUN Haiquan WANG Zhilong +1 位作者 HU Xiaoxuan XIA Wei 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2025年第6期1595-1612,共18页
Agile earth observation satellites(AEOSs)represent a new generation of satellites with three degrees of freedom(pitch,roll,and yaw);they possess a long visible time window(VTW)for ground targets and support imaging at... Agile earth observation satellites(AEOSs)represent a new generation of satellites with three degrees of freedom(pitch,roll,and yaw);they possess a long visible time window(VTW)for ground targets and support imaging at any moment within the VTW.However,different observation times demonstrate different cloud cover distributions,which exhibit different effects on the AEOS observation.Previous studies ignored pitch angles,discretized VTWs,or fixed cloud cover for every VTW,which led to the loss of intermediate observation states,thus these studies are not suitable for AEOS scheduling considering cloud cover distribution.In this study,a relationship formula between the cloud cover and observation time is proposed to calculate the cloud cover for every observation time,and a relationship formula between the observation time and pitch angle is designed to calculate the pitch angle for every observation time in the VTW.A refined model including the pitch angle,roll angle,and cloud cover distribution is established,which can make the scheme closer to the actual application of AEOSs.A hybrid genetic simulated annealing(HGSA)algorithm for AEOS scheduling is proposed,which integrates the advantages of genetic and simulated annealing algorithms and can effectively avoid falling into a local optimal solution.The experiments are conducted to compare the proposed algorithm with the traditional algorithms,the results verify that the proposed model and algorithm are efficient and effective for AEOS scheduling considering cloud cover distribution. 展开更多
关键词 agile Earth observation satellite cloud cover distribution hybrid genetic simulated annealing algorithm
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A Bi-Level Optimization Model and Hybrid Evolutionary Algorithm for Wind Farm Layout with Different Turbine Types 认领 引用
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作者 Erping Song Zipin Yao 《Energy Engineering》 EI 2025年第12期5129-5147,共19页
Wind farm layout optimization is a critical challenge in renewable energy development,especially in regions with complex terrain.Micro-siting of wind turbines has a significant impact on the overall efficiency and eco... Wind farm layout optimization is a critical challenge in renewable energy development,especially in regions with complex terrain.Micro-siting of wind turbines has a significant impact on the overall efficiency and economic viability of wind farm,where the wake effect,wind speed,types of wind turbines,etc.,have an impact on the output power of the wind farm.To solve the optimization problem of wind farm layout under complex terrain conditions,this paper proposes wind turbine layout optimization using different types of wind turbines,the aim is to reduce the influence of the wake effect and maximize economic benefits.The linear wake model is used for wake flow calculation over complex terrain.Minimizing the unit energy cost is taken as the objective function,considering that the objective function is affected by cost and output power,which influence each other.The cost function includes construction cost,installation cost,maintenance cost,etc.Therefore,a bi-level constrained optimization model is established,in which the upper-level objective function is to minimize the unit energy cost,and the lower-level objective function is to maximize the output power.Then,a hybrid evolutionary algorithm is designed according to the characteristics of the decision variables.The improved genetic algorithm and differential evolution are used to optimize the upper-level and lower-level objective functions,respectively,these evolutionary operations search for the optimal solution as much as possible.Finally,taking the roughness of different terrain,wind farms of different scales and different types of wind turbines as research scenarios,the optimal deployment is solved by using the algorithm in this paper,and four algorithms are compared to verify the effectiveness of the proposed algorithm. 展开更多
关键词 Bi-level optimization genetic algorithm differential evolution hybrid evolutionary algorithm wind farm layout
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A Hybrid MZOA-PSO Optimized Cascaded PI(1+DD)-PI-PID Controller for Frequency Stability of Interconnected Power Systems with Renewable Energy and Electric Vehicles 认领 引用
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作者 AL-Wesabi Ibrahim Hassan M.Hussein Farh +3 位作者 Jiazhu Xu Mohamad A.Alawad Ahmed Alqurashi Abdullrahman A.Al-Shamma’a 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第7期650-697,共48页
Load frequency control(LFC)in interconnected power systems has always been a challenging task in the presence of uncertainty and variability in the power systems arising primarily due to the integration of renewable e... Load frequency control(LFC)in interconnected power systems has always been a challenging task in the presence of uncertainty and variability in the power systems arising primarily due to the integration of renewable energy sources and the impact of electric vehicles on the power system.Although various PI/PID and other advanced control strategies have been employed for LFC in power systems,the existing methods have shown some limitations in terms of dynamic flexibility and robustness in the presence of nonlinearities and couplings in the power systems.Moreover,the optimization methods employed for the tuning of the controllers have shown some limitations in terms of the balance between global and local search abilities of the optimization functions.To overcome the limitations of the existing methods and optimization functions,a hybrid Modified Zebra Optimization Algorithm-Particle Swarm Optimization(MZOA-PSO)is presented in this paper for the optimization of a cascaded PI(1+DD)-PI-PID controller for LFC in power systems.The MZOA enhances the original ZOA by chaotic initialization,adaptive parameter control,and Lévy-flight foraging to improve the global search ability,while PSO ensures efficient local search ability.The optimizer is first validated using four benchmark functions,achieving the global optimum for the Booth and Zakharov functions,a mean value of 2.13×10−28 with a 98%success rate for Rosenbrock,and 3.21×10−81 for Schwefel 2.22.Under a 1%step load perturbation,the proposed controller achieves a 13 s settling time,zero negative deviation in Area 2,a maximum positive excursion of 0.10 Hz,and tie-line undershoot limited to−0.10 p.u.Under random load variations,deviations remain within±0.03 Hz and±0.02 p.u.Under RES and EV integration,the peak frequency deviation is reduced to 0.46 Hz in Area 1.These results confirm that the proposed hybrid MZOA-PSO tuned cascaded controller provides improved damping,faster stabilization,and stronger robustness for modern interconnected LFC systems. 展开更多
关键词 Load frequency control cascaded PI(1+DD)-PI-PID controller hybrid MZOA-PSO algorithm interconnected power systems performance optimization
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Federated Multi-Label Feature Selection via Dual-Layer Hybrid Breeding Cooperative Particle Swarm Optimization with Manifold and Sparsity Regularization 认领 引用
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作者 Songsong Zhang Huazhong Jin +5 位作者 Zhiwei Ye Jia Yang Jixin Zhang Dongfang Wu Xiao Zheng Dingfeng Song 《Computers, Materials & Continua》 SCIE EI 2026年第1期1141-1159,共19页
Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant chal... Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics. 展开更多
关键词 Multi-label feature selection federated learning manifold regularization sparse constraints hybrid breeding optimization algorithm particle swarm optimizatio algorithm privacy protection
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New hybrid algorithm for land surface temperature retrieval from multiple-band thermal infrared image without atmospheric and emissivity data inputs 认领 引用 被引量：3
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作者 Huazhong Ren Jiaji Dong +5 位作者 Rongyuan Liu Yitong Zheng Jinxin Guo Shanshan Chen Jing Nie Yan Zhao 《International Journal of Digital Earth》 SCIE EI 2020年第12期1430-1453,共24页
Land surface temperature(LST)retrieval from thermal infrared(TIR)remote sensing image requires atmospheric and land surface emissivity(LSE)data that are sometimes unattainable.To overcome this problem,a hybrid algorit... Land surface temperature(LST)retrieval from thermal infrared(TIR)remote sensing image requires atmospheric and land surface emissivity(LSE)data that are sometimes unattainable.To overcome this problem,a hybrid algorithm is developed to retrieve LST without atmospheric correction and LSE data input,by combining the split-window(SW)and temperature–emissivity separation(TES)algorithms.The SW algorithm is used to estimate surface-emitting radiance in adjacent TIR bands,and such radiance is applied to the TES algorithm to retrieve LST and LSE.The hybrid algorithm is implemented on five TIR bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer(ASTER).Analysis shows that the hybrid algorithm can estimate LST and LSE with an error of 0.5–1.5 K and 0.007–0.020,respectively.Moreover,the LST error of the hybrid algorithm is equivalent to that of the original ASTER TES algorithm,involving 1%–2%uncertainty in atmospheric correction.The hybrid algorithm is validated using ground-measured LST at six sites and ASTER LST products,indicating that the temperature difference between the ASTER TES algorithm and the hybrid algorithm is 1.4 K and about 2.5–3.5 K compared to the ground measurement.Finally,the hybrid algorithm is applied to at two places. 展开更多
关键词 Land surface temperature and emissivity TES algorithm split-window algorithm hybrid algorithm
Damage detection in steel plates using feed-forward neural network coupled with hybrid particle swarm optimization and gravitational search algorithm 认领 引用 被引量：2
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作者 Long Viet HO Duong Huong NGUYEN +2 位作者 Guido de ROECK Thanh BU-TIEN Magd Abdel WAHAB 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2021年第6期467-480,共14页
Over recent decades,the artificial neural networks(ANNs)have been applied as an effective approach for detecting damage in construction materials.However,to achieve a superior result of defect identification,they have... Over recent decades,the artificial neural networks(ANNs)have been applied as an effective approach for detecting damage in construction materials.However,to achieve a superior result of defect identification,they have to overcome some shortcomings,for instance slow convergence or stagnancy in local minima.Therefore,optimization algorithms with a global search ability are used to enhance ANNs,i.e.to increase the rate of convergence and to reach a global minimum.This paper introduces a two-stage approach for failure identification in a steel beam.In the first step,the presence of defects and their positions are identified by modal indices.In the second step,a feedforward neural network,improved by a hybrid particle swarm optimization and gravitational search algorithm,namely FNN-PSOGSA,is used to quantify the severity of damage.Finite element(FE)models of the beam for two damage scenarios are used to certify the accuracy and reliability of the proposed method.For comparison,a traditional ANN is also used to estimate the severity of the damage.The obtained results prove that the proposed approach can be used effectively for damage detection and quantification. 展开更多
关键词 Feedforward neural network-particle swarm optimization and gravitational search algorithm(FNN-PSOGSA) Modal damage indices Damage detection Hybrid algorithm PSOGSA
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